Application and optimization of adaptive genetic algorithm in fencing training load prediction: a data visualization-based analytical approach

This study proposes an Adaptive Genetic Algorithm (AGA) model for predicting training load in fencing. Training load is defined using external mechanical load collected from sensors and is categorized into six components: strength, aerobic, capacity, endurance, speed, agility, and flexibility. The study employs the publicly available Daily and Sports Activities dataset, which includes data from eight healthy adults (four females and four males, aged 20-30) performing 19 types of activities. Time-series segments are mapped to fencing-related load patterns for model training and evaluation. The proposed AGA dynamically adjusts the fitness function, crossover rate, and mutation rate. Its performance is compared with several models, including Deep Neural Network with Gated Recurrent Unit (DNN-GRU), Extreme Gradient Boosting (XGBoost), Long Short-Term Memory with Attention Mechanism (LSTM-Attn), Event Adversarial Neural Network (EANN), and Temporal Attention Graph Convolutional Network (TA-GCN). The results show that the AGA consistently outperformed all comparison methods in terms of prediction error and goodness-of-fit. For example, in endurance load prediction, the test set achieves an R² of 0.97 and an accuracy of 0.96. Time-series visualizations are used to analyze typical and extreme load segment, where extreme load is defined as time windows in which the predicted external load falls within the top decile. Overall, the findings demonstrate that the optimized AGA framework provides reliable training load predictions while maintaining computational efficiency. This approach offers a practical reference for data-driven training monitoring and training planning in fencing and related sports.